Eye activity-based within-task cognitive load measurement (CLM) is currently not feasible in everyday situations. One important issue to be addressed to move such CLM beyond controlled laboratory environments is determining practical methods for mitigating the pupillary light reflex (PLR) effect in CLM. In this paper, four approaches to dealing with the PLR effect within a modified verbal digit span task are investigated: ignore the PLR, exclude PLR data, compensate for PLR and use PLR features for measurement. During experimental work, cognitive load and the PLR were induced with a modified verbal digit span task and changes in brightness of a large monitor, respectively. The “exclude PLR,” “compensate for PLR,” and “use PLR features” methods were found to improve classification performance by up to 18.5% relative to the “ignore PLR” method, which yielded the worst classification accuracy of 58% using an average pupil diameter feature. Features derived from the transient properties of the PLR response associated with cognitive load were found to yield the superior classification accuracy of 70%, which is an improvement compared with previously published approaches which treated the PLR responses as interference. The findings from this paper suggest that the PLR cannot be easily ignored or normalized, and clearly demonstrate the importance of PLR-aware feature extraction for the design of future eyewear-based always-on CLM in conditions that are more realistic than a darkened, controlled laboratory.

中文翻译：

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基于眼镜的认知负荷测量缓解任务内亮度变化的方法比较

基于眼部活动的任务内认知负荷测量 (CLM) 目前在日常情况下不可行。将此类 CLM 移出受控实验室环境需要解决的一个重要问题是确定减轻 CLM 中瞳孔光反射 (PLR) 效应的实用方法。在本文中，研究了在修改后的语言数字跨度任务中处理 PLR 效应的四种方法：忽略 PLR、排除 PLR 数据、补偿 PLR 和使用 PLR 特征进行测量。在实验工作期间，认知负荷和 PLR 分别通过修改后的语言数字跨度任务和大型显示器的亮度变化引起。发现“排除 PLR”、“补偿 PLR”和“使用 PLR 特征”方法相对于“忽略 PLR”方法可将分类性能提高多达 18.5%，使用平均瞳孔直径特征产生了 58% 的最差分类精度。发现源自与认知负荷相关的 PLR 响应的瞬态特性的特征产生了 70% 的卓越分类准确度，与之前发布的将 PLR 响应视为干扰的方法相比，这是一个改进。本文的研究结果表明，PLR 不能轻易被忽略或归一化，并清楚地证明了 PLR 感知特征提取对于未来基于眼镜的永远在线 CLM 设计的重要性，这些条件比变暗、受控的条件更真实。实验室。发现源自与认知负荷相关的 PLR 响应的瞬态特性的特征产生了 70% 的卓越分类准确度，与之前发布的将 PLR 响应视为干扰的方法相比，这是一个改进。本文的研究结果表明，PLR 不能轻易被忽略或归一化，并清楚地证明了 PLR 感知特征提取对于未来基于眼镜的永远在线 CLM 设计的重要性，这些条件比变暗、受控的条件更真实。实验室。发现源自与认知负荷相关的 PLR 响应的瞬态特性的特征产生了 70% 的卓越分类准确度，与之前发布的将 PLR 响应视为干扰的方法相比，这是一个改进。本文的研究结果表明，PLR 不能轻易被忽略或归一化，并清楚地证明了 PLR 感知特征提取对于未来基于眼镜的永远在线 CLM 设计的重要性，这些条件比变暗、受控的条件更真实。实验室。